Multi-class classification: mirror descent approach

06/30/2016
by   Daria Reshetova, et al.
0

We consider the problem of multi-class classification and a stochastic opti- mization approach to it. We derive risk bounds for stochastic mirror descent algorithm and provide examples of set geometries that make the use of the algorithm efficient in terms of error in k.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/23/2013

Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization

Wepresentanovelcolumngenerationbasedboostingmethod for multi-class class...
research
05/24/2019

A Generalization Error Bound for Multi-class Domain Generalization

Domain generalization is the problem of assigning labels to an unlabeled...
research
03/28/2015

Active Model Aggregation via Stochastic Mirror Descent

We consider the problem of learning convex aggregation of models, that i...
research
08/29/2011

Datum-Wise Classification: A Sequential Approach to Sparsity

We propose a novel classification technique whose aim is to select an ap...
research
11/14/2019

An Application of Multiple-Instance Learning to Estimate Generalization Risk

We focus on several learning approaches that employ max-operator to eval...
research
06/23/2022

Inductive Conformal Prediction: A Straightforward Introduction with Examples in Python

Inductive Conformal Prediction (ICP) is a set of distribution-free and m...
research
10/18/2011

AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem

This paper presents an improvement to model learning when using multi-cl...

Please sign up or login with your details

Forgot password? Click here to reset